VOL. 10, NO. 3, FEBRUARY 2015 ISSN 1819-6608 ARPN Journal of Engineering and Applied Sciences © 2006-2015 Asian Research Publishing Network (ARPN). All rights reserved. www.arpnjournals.com 1352 IMMUNE NETWORK ALGORITHM IN MONTHLY STREAMFLOW PREDICTION AT JOHOR RIVER Nur Izzah Mat Ali 1 , M. A. Malek 2 , Amelia Ritahani Ismail 3 1 Department of Civil Engineering, Universiti Tenaga Nasional, Kajang, Malaysia 2 Institute of Energy, Policy and Research (IEPRe), Universiti Tenaga Nasional, Kajang, Malaysia 3 Department of Computer Science, Kuliyyah of Information and Communication Technology, International Islamic University Malaysia, Kuala Lumpur, Malaysia E-Mail: izzah5255@gmail.com ABSTRACT This study proposes an alternative method in generating future stream flow data with single-point river stage. Prediction of stream flow data is important in water resources engineering for planning and design purposes in order to estimate long term forecasting. This paper utilizes Artificial Immune System (AIS) in modelling the stream flow of one stations of Johor River. AIS has the abilities of self-organizing, memory, recognition, adaptive and ability of learning inspired from the immune system. Immune Network Algorithm is part of the three main algorithm in AIS. The model of Immune Network Algorithm used in this study is aiNet. The training process in aiNet is partly inspired by clonal selection principle and the other part uses antibody interactions for removing redundancy and finding data patterns. Like any other traditional statistical and stochastic techniques, results from this study, exhibit that, Immune Network Algorithm is capable of producing future stream flow data at monthly duration with various advantages. Keywords: immune network algorithm, artificial immune system, streamflow prediction. INTRODUCTION Streamflow forecasts is crucial to flood mitigation and water assets administration and arrangement. While transient expectation, for example, hour or every day guaging is essential for surge cautioning and resistance, long haul forecast focused around month to month, occasionally or yearly time scales is exceptionally helpful in store operations and watering system administration choices, for example, planning discharges, apportioning water to downstream clients, dry season moderation and overseeing stream bargains or executing conservative compliance [1]. A critical number of gauging models and approaches have been created and connected with this field because of the imperatives of hydrologic forecasting. These streamflow forecasting models can be categorized as process-driven methods and data-driven methods [2]. Linear models such as AutoRegressive (AR), AutoRegressive Moving Average (ARMA), AutoRegressive Integrated Moving Average (ARIMA), and Seasonal ARIMA (SARIMA) had made a great success in streamflow prediction[3]. Artificial Neural Networks (ANNs), Genetic Algorithms and Artificial Immune Systems (AIS) are some of streamflow prediction techniques which have grown popularity lately. In this study the anticipated future streamflow information will be utilized for estimating of water assets arranging and operational frameworks. This anticipated streamflow information is extremely valuable for long haul guaging in the arranging and operation of the water assets administration. As an expansion, the utilization of the propose technique i.e Artificial Immune System will be another commitment to the field of hydrology in anticipating month to month streamflow information. The objective of this study is to develop and test the feasibility and accuracy of the monthly streamflow prediction model using an Artificial Immune System (AIS). ARTIFICIAL IMMUNE SYSTEM AIS was characterized as versatile frameworks, propelled by hypothetical immunology and watched resistant capacities, standards and models, which are connected to problem solving [4]. There are many conceptions and opinion that have been taken out from the biological immune systems to develop new set of computer instructions to apply as authentic world engineering and scientific quandaries solver. The external microorganism is defended by the immune system from attacking the human bodies, as it is the main role of the immune system. [4]. Two types of immune system immunity, which is innate and adaptive immune system. Both systems are formed of two main lines of defense in the immune system[5]. It is capable to nearly recognize any pathogen or foreign or molecules and eliminate them from body [6]. The main applications of AIS that had been done before are data mining [7], pattern recognition [8], anomaly detection [9] and scheduling [10]. AIS has three main algorithms which are clonal selection algorithm (CSA), immune network algorithm (INA) and negative selection algorithm (NSA) [11][12][13]. INA for the most part connected to manage dynamic circumstance and improvement emergency where NSA generally fruitful applying its methodologies in abnormality identification [14]. Clonal Selection Principle is satisfactory in taking care of the issue with respect to scheduling and optimization [14]. This study uses INA in AIS to foresee month to month streamflow data[1].